Wetness modulates the effects of grazing on net ecosystem productivity in global grasslands

  1. Yueqiang Wu
  2. Le Qi
  3. Hao Li
  4. Jiguang Feng
  5. Peng Zhou
  6. Hangyu Li
  7. Xiaoyang Gao
  8. Zhijie Wang
  9. Shilin Cui
  10. Ping Yin
  11. Wenhong Ma
  12. Cunzhu Liang
  13. Zhiyong Li
  14. Biao Zhu

  • Curated by eLife

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    eLife Assessment

    This study uses convincing modeling methods and analyses of rich behavioral datasets to investigate the role of attention in value-based decision making; for instance, as when choosing between two snacks. The results are valuable, as they challenge existing theories that assume that paying attention to an available option biases the eventual choice toward that option. The results suggest that the correlation between attention and decision-making is formed largely after and not before the (internal) choice process has terminated, a finding that offers an intuitively appealing rethinking of how attention and decision-making processes interact during value-based choices.

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Abstract

Overgrazing has caused widespread global concern. However, how grazing intensity affects ecosystem carbon dioxide (CO 2 ) fluxes with wetness fluctuations in grasslands at a global scale remains poorly understood. Here we measured ecosystem CO 2 fluxes covering the 7th to 11th years of a continuous grazing experiment in a typical steppe, and conducted a meta-analysis of 585 observations by collecting relevant data in global grasslands. Our experimental results showed that mean gross primary productivity (GPP) and ecosystem respiration (ER) were decreased under heavy grazing (HG) compared with control without grazing (CK), whereas net ecosystem productivity (NEP) was not different between HG and CK in the typical steppe. The NEP was higher under light grazing in the wetter years, but it was not different between LG and CK under drier years in the typical steppe. In contrast, grazing decreased GPP, ER and NEP, respectively, in global grasslands. Both our field experiment and the meta-analysis revealed that the response of NEP to light grazing was linearly correlated with the wetness index, which may be mediated by greater plant relative growth rate in wetter years. Higher wetness and aboveground biomass (AGB) increased the response of NEP to grazing in global grasslands. However, heavy grazing reduced NEP and AGB even under higher wetness indices in global grasslands, resulting from a loss of their resilience in long-term heavy grazing. These findings indicate that light grazing appears to be a promising management to promote plant relative growth rate and CO 2 flux sequestration. Furthermore, major drivers influencing ecosystem CO 2 fluxes in response to grazing were aboveground biomass, grazing intensity and wetness index. Overall, this study provides a global perspective on the response of ecosystem CO 2 fluxes to grazing intensity and improves our knowledge of the factors influencing the response of ecosystem CO 2 fluxes to grazing intensity.

Article activity feed

  1. eLife

    eLife Assessment

    This study uses convincing modeling methods and analyses of rich behavioral datasets to investigate the role of attention in value-based decision making; for instance, as when choosing between two snacks. The results are valuable, as they challenge existing theories that assume that paying attention to an available option biases the eventual choice toward that option. The results suggest that the correlation between attention and decision-making is formed largely after and not before the (internal) choice process has terminated, a finding that offers an intuitively appealing rethinking of how attention and decision-making processes interact during value-based choices.

  2. eLife

    Reviewer #1 (Public review):

    Summary:

    This study examines whether gaze direction actively shapes choice during food preference decisions or whether gaze and choice evolve largely independently until the moment of commitment. The established framework in this context, the aDDM, assumes that gaze causally biases the accumulation of evidence in favour of the fixated item. The authors show convincingly that this model fails to fit key behavioural patterns across several datasets, as do other published models that make the same assumption. The authors propose an alternative model (Post-Decision-Gaze or PDG) in which gaze and decision formation are decoupled: gaze does not influence the decision process, nor is it drawn toward the ultimately chosen item, until after the decision threshold is reached. Only during the motor execution period (after commitment) is gaze directed to the chosen option. They demonstrate that this model fits several observed patterns better than the aDDM and related variants.

    Strengths:

    The work thoroughly considers multiple models and datasets. It advances an interesting alternative perspective on gaze-decision interactions and highlights meaningful shortcomings in existing models. The authors take the time to explain how modelling assumptions produce specific patterns in the data, which is certainly insightful to readers interested in the modelling of value-based decision making.

    Weaknesses:

    It is unclear to what extent the model's success relies on the way non-decision time is formalised in the model. In the proposed PDG model, non-decision time is decomposed into separate visual encoding, saccadic execution, and manual execution components. Several values (assumed or recovered) do not match known physiological or behavioural ranges. This is a common issue in the literature, and the authors may want to address it in light of broader work discussing what non-decision time consists of in both manual and saccadic actions (e.g., Bompas et al., 2024, Non decision time: the Higgs boson of decision, Psychological Review).

    In particular, the "saccadic execution" parameter appears far too long and too variable to reflect merely execution; instead, it likely includes decisional components. This would make more sense since manual and saccadic planning essentially rely on distinct brain areas, hence it seems unrealistic that crossing a single threshold would trigger both manual and saccadic execution. Similarly, recovered manual non-decision times are substantially longer (though not more variable) than expected motor execution durations for button presses. These patterns suggest that parts of what the model treats as non-decision time are likely decisional in nature, although perhaps related to "action decision" rather than the "value-based decision" of interest to the authors. To what extent these two processes neatly follow each other or overlap could be usefully considered.

  3. eLife

    Reviewer #2 (Public review):

    Summary:

    Zylberberg et al. reanalyze eye-tracking and behavioral data (mostly from Krajbich et al., 2010) to test two predictions of the attentional Drift Diffusion Model, finding that these predictions are not met. Similarly, predictions of normative models (inspired by rational inattention) are not in line with the data, and the authors propose a post-choice model of attention. This model better accounts for the two effects but also does not account for all patterns, so the authors conclude that eye movements most likely reflect both pre- and post-decisional processes.

    Strengths:

    A clear strength is the systematic falsification-based approach of the paper, establishing (partially) new predictions and testing to what extent these are met by extant models and by a newly developed theory. The authors do a good job in providing intuitions behind the effects and the reasons why models such as the aDDM predict them. The paper is of substantial relevance for the field, as it shows that effects pertaining to the last fixation(s) should be interpreted with caution. Another strength is the paper's transparency as the authors clearly acknowledge that their new model does not do a perfect job either.

    Weaknesses:

    The paper focuses on analyzing the Krajbich 2010 data, but shows that the second effect replicates in many other datasets. A more principled approach, in which both effects are analyzed and presented for all datasets, would be more convincing. The results should then be shown together for clarity/readability.

    Similarly, it would be nice to show to what extent the models' predictions depend (not depend) on using the best-fitting parameter values (are there any parameter settings under which the two effects are not predicted?)

  4. eLife

    Reviewer #3 (Public review):

    Summary:

    In this study, the authors reanalyzed choice, RT and gaze datasets collected from human subjects performing a food-choice task. They show that models that posit a causal role for attention in shaping the decision-making process fail to account for empirical observations in the data. These include the attentional drift diffusion model (aDDM) and models that derive attention-choice associations from an optimal policy. The authors show that a model that assumes that gazes are directed towards the chosen option after decision commitment captures more (but not all) empirical findings, suggesting that attention may reflect decisions once they are made instead of contributing to their formation. However, this post-decision-gaze (PDG) model failed to capture all aspects of the data, suggesting that gaze may reflect both decisional and post-decisional operations, and existing models are still missing some features of the gaze-directing process. The authors provide convincing evidence that post-decision gaze explains a number of empirical findings in this task.

    Strengths:

    (1) The analyses are generally appropriate, and the conclusions are supported by the data.

    (2) The study was rigorous, as the authors considered a number of alternative possible models for behavior, and evaluated their performance based on a wide range of qualitative predictions (as opposed to exclusively relying on model comparison).

    (3) The proposal that gaze may largely reflect post-decisional processes is interesting, and as far as I am aware, novel.

    Weaknesses:

    There was limited discussion about why one might allocate attention post-decision. I would have appreciated more discussion on the potential functional consequences or implications of post-decision gaze.

  5. Version published to 10.64898/2026.01.27.701613 on bioRxiv